import pandas as pd
import numpy as np
import plotly.express as px 
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

data=pd.read_csv('C:/Users/Rakesh/Datasets/BRCA.csv')

data.head()

	Patient_ID	Age	Gender	Protein1	Protein2	Protein3	Protein4	Tumour_Stage	Histology	ER status	PR status	HER2 status	Surgery_type	Date_of_Surgery	Date_of_Last_Visit	Patient_Status
0	TCGA-D8-A1XD	36.0	FEMALE	0.080353	0.42638	0.54715	0.273680	III	Infiltrating Ductal Carcinoma	Positive	Positive	Negative	Modified Radical Mastectomy	15-Jan-17	19-Jun-17	Alive
1	TCGA-EW-A1OX	43.0	FEMALE	-0.420320	0.57807	0.61447	-0.031505	II	Mucinous Carcinoma	Positive	Positive	Negative	Lumpectomy	26-Apr-17	09-Nov-18	Dead
2	TCGA-A8-A079	69.0	FEMALE	0.213980	1.31140	-0.32747	-0.234260	III	Infiltrating Ductal Carcinoma	Positive	Positive	Negative	Other	08-Sep-17	09-Jun-18	Alive
3	TCGA-D8-A1XR	56.0	FEMALE	0.345090	-0.21147	-0.19304	0.124270	II	Infiltrating Ductal Carcinoma	Positive	Positive	Negative	Modified Radical Mastectomy	25-Jan-17	12-Jul-17	Alive
4	TCGA-BH-A0BF	56.0	FEMALE	0.221550	1.90680	0.52045	-0.311990	II	Infiltrating Ductal Carcinoma	Positive	Positive	Negative	Other	06-May-17	27-Jun-19	Dead

data.isnull().sum()

Patient_ID             7
Age                    7
Gender                 7
Protein1               7
Protein2               7
Protein3               7
Protein4               7
Tumour_Stage           7
Histology              7
ER status              7
PR status              7
HER2 status            7
Surgery_type           7
Date_of_Surgery        7
Date_of_Last_Visit    24
Patient_Status        20
dtype: int64

data=data.dropna()

data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 317 entries, 0 to 333
Data columns (total 16 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Patient_ID          317 non-null    object 
 1   Age                 317 non-null    float64
 2   Gender              317 non-null    object 
 3   Protein1            317 non-null    float64
 4   Protein2            317 non-null    float64
 5   Protein3            317 non-null    float64
 6   Protein4            317 non-null    float64
 7   Tumour_Stage        317 non-null    object 
 8   Histology           317 non-null    object 
 9   ER status           317 non-null    object 
 10  PR status           317 non-null    object 
 11  HER2 status         317 non-null    object 
 12  Surgery_type        317 non-null    object 
 13  Date_of_Surgery     317 non-null    object 
 14  Date_of_Last_Visit  317 non-null    object 
 15  Patient_Status      317 non-null    object 
dtypes: float64(5), object(11)
memory usage: 42.1+ KB

data.describe()

	Age	Protein1	Protein2	Protein3	Protein4
count	317.000000	317.000000	317.000000	317.000000	317.000000
mean	58.725552	-0.027232	0.949557	-0.095104	0.006713
std	12.827374	0.543858	0.906153	0.589027	0.625965
min	29.000000	-2.144600	-0.978730	-1.627400	-2.025500
25%	49.000000	-0.350600	0.368840	-0.531360	-0.382240
50%	58.000000	0.005649	0.997130	-0.193040	0.038522
75%	67.000000	0.336260	1.612000	0.251210	0.436250
max	90.000000	1.593600	3.402200	2.193400	1.629900

stage=data['Tumour_Stage'].value_counts()
transactions = stage.index
quantity = stage.values

figure = px.pie(data, values=quantity, names=transactions, hole=0.5, title='Tumour Stages of Patients')
figure.show()

# Tumour Stage
histology = data["Histology"].value_counts()
transactions = histology.index
quantity = histology.values

figure = px.pie(data, 
             values=quantity, 
             names=transactions,hole = 0.5, 
             title="Histology of Patients")
figure.show()

data['ER status'].value_counts()

Positive    317
Name: ER status, dtype: int64

data['PR status'].value_counts()

Positive    317
Name: PR status, dtype: int64

data['HER2 status'].value_counts()

Negative    288
Positive     29
Name: HER2 status, dtype: int64

surgery = data["Surgery_type"].value_counts()
transactions = surgery.index
quantity = surgery.values

figure = px.pie(data, 
             values=quantity, 
             names=transactions,hole = 0.5, 
             title="Types of Surgery for Patients")
figure.show()

data['Tumour_Stage'] = data['Tumour_Stage'].map({'I':1,'II':2,'III':3})
data['Histology'] = data['Histology'].map({'Infiltrating Ductal Carcinoma':1, 'Infiltrating Lobular Carcinoma':2,"Mucinous Carcinoma": 3})

data['ER status'] = data['ER status'].map({'Positive':1})
data['PR status'] = data['PR status'].map({'Positive':1})

data['HER2 status'] = data['HER2 status'].map({'Positive': 1,'Negative': 2})

data['Gender'] = data['Gender'].map({'MALE':0, 'FEMALE':1})
data['Surgery_type'] = data['Surgery_type'].map({'Other':1, 'Modified Radical Mastectomy': 2, 'Lumpectomy':3, 'Simple Mastectomy':4})

data.head()

	Patient_ID	Age	Gender	Protein1	Protein2	Protein3	Protein4	Tumour_Stage	Histology	ER status	PR status	HER2 status	Surgery_type	Date_of_Surgery	Date_of_Last_Visit	Patient_Status
0	TCGA-D8-A1XD	36.0	1	0.080353	0.42638	0.54715	0.273680	3	1	1	1	2	2	15-Jan-17	19-Jun-17	Alive
1	TCGA-EW-A1OX	43.0	1	-0.420320	0.57807	0.61447	-0.031505	2	3	1	1	2	3	26-Apr-17	09-Nov-18	Dead
2	TCGA-A8-A079	69.0	1	0.213980	1.31140	-0.32747	-0.234260	3	1	1	1	2	1	08-Sep-17	09-Jun-18	Alive
3	TCGA-D8-A1XR	56.0	1	0.345090	-0.21147	-0.19304	0.124270	2	1	1	1	2	2	25-Jan-17	12-Jul-17	Alive
4	TCGA-BH-A0BF	56.0	1	0.221550	1.90680	0.52045	-0.311990	2	1	1	1	2	1	06-May-17	27-Jun-19	Dead

x=np.array(data[['Age', 'Gender', 'Protein1', 'Protein2', 'Protein3','Protein4', 
                   'Tumour_Stage', 'Histology', 'ER status', 'PR status', 
                   'HER2 status', 'Surgery_type']])
y=np.array(data[['Patient_Status']])
xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.1, random_state=42)

model= SVC()

model.fit(xtrain, ytrain)

C:\Users\Rakesh\Downloads\Anaconda\lib\site-packages\sklearn\utils\validation.py:993: DataConversionWarning:

A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

SVC()

features = np.array([[36.0, 1, 0.080353, 0.42638, 0.54715, 0.273680, 3, 1, 1, 1, 2, 2,]])
print(model.predict(features))

['Alive']